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- W4386870443 abstract "The massive boost in the technology industry proves as a boon for data science researchers. In the past few decades, new emerging advancements in the field of ML, Deep learning, and image pre-processing makes it possible to automate the task of different fields which required humans rather expert humans’ involvement. Since the advent of neural networking, there has been an enormous demand for acquiring information and generating datasets to support various research projects and to train module work for supporting artificial learning, but due to the lack of diversity in datasets, there are major hurdles during data preparation. There is a need to develop a system that could provide the visual dataset for this communication learning for machine algorithms. To overcome this bottleneck of the neural network, GAN (Generative Adversarial Networks) were created to acquire fake data with the objective of anonymizing users’ information to generate a huge stack of data representations. But working on GAN is not an easy task because it requires a deep understanding of deep learning and image pre-processing. In this paper, a dataset is included to support work for the deaf community which uses sign language to communicate. The task is to read hand signals by detecting the movements and visualizing the communication mode through movements of fingers and to achieve that, personal photos have been captured which are provided as input samples to GAN based model. The various requirements of future work in the same field will require a vast amount of hand sign data which is lacking in the current domain as there have been limitations with different backgrounds, shortage of volunteers, and different gestures to portray generalized reading and storage of such data in a variable format of hand signs have been raised as an issue by many research workers. This data exists in mere thousands and scrapped way over the digitalized media so a methodology is provided to resolve this problem through a tool that will create as many amounts of images datasets as required and, this will provide a healthier feed for machine training that will support future as well as current data requirements for work going on for the deaf community." @default.
- W4386870443 created "2023-09-20" @default.
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- W4386870443 date "2023-01-01" @default.
- W4386870443 modified "2023-09-27" @default.
- W4386870443 title "Fake Image Dataset Generation of Sign Language Using GAN" @default.
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- W4386870443 doi "https://doi.org/10.1007/978-981-99-5080-5_10" @default.
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